Browse Prior Art Database

Using the seismic detection range bias and magnitude frequency relationship as a quality control tool on microseismic event dataset

IP.com Disclosure Number: IPCOM000243873D
Publication Date: 2015-Oct-23
Document File: 3 page(s) / 166K

Publishing Venue

The IP.com Prior Art Database

Abstract

A Microseismic event dataset mostly contains information of the microseismic event timing location and magnitude Occasionally more advanced attributes such as moment tensor information are also included As processing results we need ways to quality control the dataset understand its quality and therefore limitations in its future application The standard way to quality control processing result is to calculate the synthetic travel time from the event timing location info using the adopted velocity structure to match the picked P and or S arrival on the seismogram In this type of quality control the event timing location uncertainty could be estimated but not magnitude or event size related attributes However the event size related attributes such as magnitudes are key parameters for stimulation efficiency estimation therefore is essential for judging the quality of a processed microseismic dataset Here we propose an easy applicable two step quality controlling workflow on the processed microseismic event dataset In these quality control procedures no seismogram fitting original recorded signal is involved only the event dataset and receiver locations are concerned First using the magnitude versus detection distance plot to see if the data gives an physically meaningful detection range bias trend Second fitting the Gutenberg Richter law with the test dataset the quality of the fitting and its reflected physical relation can used as indicators of the processing quality as well

This text was extracted from a Microsoft Word document.
At least one non-text object (such as an image or picture) has been suppressed.
This is the abbreviated version, containing approximately 43% of the total text.

Using the seismic detection range bias and magnitude frequency relationship as a quality control tool on microseismic event dataset

1         Abstract

A Microseismic event dataset mostly contains information of the microseismic event timing, location and magnitude. Occasionally, more advanced attributes, such as moment tensor information, are also included. As processing results, we need ways to quality control the dataset, understand its quality and therefore limitations in its future application. The standard way to quality control processing result is to calculate the synthetic travel time from the event timing & location info, using the adopted velocity structure, to match the picked P and/or S arrival on the seismogram. In this type of quality control, the event timing & location uncertainty could be estimated, but not magnitude, or event size related attributes. However, the event size related attributes, such as magnitudes, are key parameters for stimulation efficiency estimation, therefore is essential for judging the quality of a processed microseismic dataset. Here, we propose an easy applicable two-step quality controlling workflow on the processed microseismic event dataset. In these quality control procedures, no seismogram fitting (original recorded signal) is involved, only the event dataset and receiver locations are concerned. First, using the magnitude versus detection distance plot to see if the data gives an physically meaningful detection range bias trend; Second, fitting the Gutenberg-Richter law with the test dataset, the quality of the fitting and its reflected physical relation can used as indicators of the processing quality as well.

2         Background knowledge

2.1        Detection range bias

In simple cases, when only one string down hole vertical receiver array(often composed of multiple geophones and/or hydrophones )  is involved, the event detection limit, minimum magnitude detected, is often a function of the distance from the receiver array as in Fig.1a. With more complicated monitoring array settings, for a given location, event detection limit can still have a linear relationship with distance to receivers, but more factors are involved, such as signal to noise ratio difference with surface and downhole receiver, etc. The detection range bias in microseismic dataset is a nature heritage, which should always present in the dataset unless either a high uniform magnitude cut off has been applied or blunders occurred during the processing steps. The latter is what we wish to avoid.

Figure 1, sketches for detection range bias. a) A microseismic dataset with correct detection limit trend. The minimum detected event magnitude increases with distance to the receiver array, i.e. more smaller events are not detected as the distance to the receiver array increases.  b) a microseismic dataset with wrong detection limit trend. The minimum detected event magnitude decreases with distance to the receiver array, i.e. more smaller events are d...